Causal Learning with Local Computations

• Published in: Journal of experimental psychology. Learning, memory, and cognition Vol. 35; no. 3; pp. 678 - 693
• Main Authors: Fernbach, Philip M, Sloman, Steven A
• Format: Journal Article
• Language: English
• Published: United States American Psychological Association 01.05.2009
• Subjects: Association Learning; Bayesian Statistics; Causal Models; Causality; Cues; Decision Making; Feedback; Heuristics; Humans; Imagination; Inferences; Knowledge of Results (Psychology); Memory; Mental Recall; Motion Perception; Orientation; Practice (Psychology); Problem Solving
• ISSN: 0278-7393
• DOI: 10.1037/a0014928

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. (Contains 13 figures and 1 table.)